Checking Distribution for Variables

Plot and fit distributions of variables

Histogram of Velocity of all

Log distribution, 0’s make up 3% of all data

CC.TotalData <- na.omit(TotalData)
head(CC.TotalData)

10^mean(log10(CC.TotalData$v[CC.TotalData$v>0]))

[1] 0.03502632

sd(log10(CC.TotalData$v[CC.TotalData$v>0]))

[1] 0.5358195

Now we’re going to plot each unique krill and their bimodality with a dip test (less than 0.05 is multimodality)

for (i in 1:length(ind)){
jpeg(filename=paste(ind[i]+".jpg"), width = 480, height = 480)
 hist(log10(CC.TotalData$v[CC.TotalData$D_V_T==ind[i]]),
     breaks = 50,
     xlab = "Velocity (Log^10 mm/s)",
     main = ind[i])
dev.off()
}

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Now looking at turning angles

for (i in 1:length(ind)){
jpeg(filename=paste(ind[i], '.jpeg', sep = ''), width = 480, height = 480)
hist(CC.TotalData$turn.angle[CC.TotalData$D_V_T==ind[i]],
       breaks = 50,
     xlab = "Turn Angles",
     main = ind[i]) 
dev.off
}

Error in jpeg(filename = paste(ind[i], ".jpeg", sep = ""), width = 480,  : 
  too many open devices

Starting to look at bimodality in the variables by merging the TotalData frame with the “tab” table

save.image("~/Post-doc/Data/Total Merged Data File (Dec 6 2021).RData")